Studies on Fuzzy Information Measures

Fuzzy information measures play an important part in measuring the similarity degree between two pattern vectors in fuzzy circumstance. In this paper, two new fuzzy information measures are set up. Firstly, the classical similarity measures, such as dissimilarity measure (DM) and similarity measure (SM) are studied, an axiom theory about fuzzy entropy is surveyed, and all kinds of definitions of fuzzy entropy are discussed. Secondly, based on the idea of Shannon information entropy, two concepts of fuzzy joint entropy and fuzzy conditional entropy are proposed and the basic properties of them are given and proved. At last, two new measures, fuzzy absolute information measure (FAIM) and fuzzy relative information measure (FRIM), are set up, which can be used to measure the similarity degree between a fuzzy set A and a fuzzy set B. So, It provides a new research approach for studies on pattern similarity measure.